research-article

AnchorMF: towards effective event context identification

Authors:

Dirk GrunwaldAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 629 - 638

https://doi.org/10.1145/2505515.2505548

Published: 27 October 2013 Publication History

Abstract

Online social networks (OSNs) such as Twitter provide a good platform for event discussions. Recent research [26][25] as shown that event discussions in OSNs are diverse and innovative and encourage public engagement in events. Although much research has been conducted in OSNs to track and detect events, there has been limited research on detecting or understanding the event context. Event context helps to better predict users' participation in events, identify relations among events, and recommend friends who share similar event context.

In this work, we have developed AnchorMF, a matrix factorization based technique that aims to identify event context by leveraging a prevalent feature in OSNs, the anchor information. Our AnchorMF work makes three key contributions: (1) a formal definition of the event context identification problem; (2) anchor selection and incorporation into the matrix factorization process for effective event context identification; and (3) demonstration of applying event context for user-event participation prediction, relevant events retrieval, and friendship recommendation. Evaluation based on 1.1 million Twitter users over a one-month data collection period shows that AnchorMF achieves a 20.0% improvement in terms of user-event participation prediction.

References

[1]

J. Allan, R. Papka, and V. Lavrenko. On-line new event detection and tracking. SIGIR '98, pages 37--45.

Digital Library

[2]

L. Chen and A. Roy. Event detection from Flickr data through wavelet-based spatial analysis. CIKM '09, pages 523--532.

Digital Library

[3]

A. Clauset, C. R. Shalizi, and M. E. Newman. Power-law distributions in empirical data. SIAM review, 51(4):661--703, 2009.

Digital Library

[4]

G. P. C. Fung, J. X. Yu, P. S. Yu, and H. Lu. Parameter free bursty events detection in text streams. VLDB '05, pages 181--192.

Digital Library

[5]

M. Gartrell, U. Paquet, and R. Herbrich. A Bayesian treatment of social links in recommender systems. CU Technical Report CU-CS-1092-12, 2012.

[6]

S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (6):721--741, 1984.

Digital Library

[7]

M. Gupta, J. Gao, C. Zhai, and J. Han. Predicting future popularity trend of events in microblogging platforms. volume 49 of Proceedings of the American Society for Information Science and Technology, pages 1--10.

[8]

Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. ICDM'08, pages 263--272.

Digital Library

[9]

J. Huang, X.-Q. Cheng, J. Guo, H.-W. Shen, and K. Yang. Social recommendation with interpersonal inuence. ECAI '10, pages 601--606.

Digital Library

[10]

A. Ihler, J. Hutchins, and P. Smyth. Adaptive event detection with time-varying Poisson processes. KDD '06, pages 207--216.

Digital Library

[11]

M. Jamali and M. Ester. A matrix factorization technique with trust propagation for recommendation in social networks. RecSys '10, pages 135--142.

Digital Library

[12]

M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu, and S. Yang. Social contextual recommendation. CIKM '12, pages 45--54.

Digital Library

[13]

J. Kleinberg. Bursty and hierarchical structure in streams. KDD '02, pages 91--101.

Digital Library

[14]

H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or a news media? WWW '10, pages 591--600.

Digital Library

[15]

V. Lampos, T. De Bie, and N. Cristianini. Flu detector-tracking epidemics on Twitter. In Machine Learning and Knowledge Discovery in Databases, pages 599--602. 2010.

Digital Library

[16]

T. Lappas, B. Arai, M. Platakis, D. Kotsakos, and D. Gunopulos. On burstiness-aware search for document sequences. KDD '09, pages 477--486.

Digital Library

[17]

C. X. Lin, B. Zhao, Q. Mei, and J. Han. Pet: A statistical model for popular events tracking in social communities. KDD'10, pages 929--938.

Digital Library

[18]

H. Ma, I. King, and M. R. Lyu. Learning to recommend with social trust ensemble. SIGIR '09, pages 203--210.

Digital Library

[19]

H. Ma, H. Yang, M. R. Lyu, and I. King. Sorec: Social recommendation using probabilistic matrix factorization. CIKM'08, pages 931--940.

Digital Library

[20]

M. C. Mozer, B. Link, and H. Pashler. An unsupervised decontamination procedure for improving the reliability of human judgments. NIPS '11, pages 1791--1799.

[21]

S. Petrović, M. Osborne, and V. Lavrenko. Streaming first story detection with application to Twitter. HLT '10, pages 181--189.

Digital Library

[22]

T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes Twitter users: Real-time event detection by social sensors. WWW '10, pages 851--860.

Digital Library

[23]

R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. ICML '08, pages 880--887.

Digital Library

[24]

R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. Advances in neural information processing systems, 20:1257--1264, 2008.

[25]

K. Starbird and L. Palen. (How) will the revolution be retweeted?: Information di usion and the 2011 Egyptian uprising. CSCW '12, pages 7--16.

Digital Library

[26]

K. Starbird, L. Palen, A. L. Hughes, and S. Vieweg. Chatter on the red: What hazards threat reveals about the social life of microblogged information. CSCW '10, pages 241--250.

Digital Library

[27]

Twitter. Following rules and best practices. https://support.twitter.com/articles/ 68916-following-rules-and-best-practices, 2013.

[28]

X. Wang, C. Zhai, X. Hu, and R. Sproat. Mining correlated bursty topic patterns from coordinated text streams. KDD '07, pages 784--793.

Digital Library

[29]

W. E. Webber. Measurement in information retrieval evaluation. 2011.

[30]

J. Weng and B.-S. Lee. Event detection in twitter. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media, volume 3, 2011.

[31]

Y. Yang, T. Ault, T. Pierce, and C. W. Lattimer. Improving text categorization methods for event tracking. SIGIR '00, pages 65--72.

Digital Library

[32]

Y. Yang, T. Pierce, and J. Carbonell. A study of retrospective and on-line event detection. SIGIR '98, pages 28--36.

Digital Library

Cited By

Magdy A(2020)MicroblogsSIGSPATIAL Special10.1145/3404820.340482712:1(41-52)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3404820.3404827
Wu CLi LPeng CWu YXiong NLee C(2019)Design and analysis of an effective graphics collaborative editing systemEURASIP Journal on Image and Video Processing10.1186/s13640-019-0427-62019:1Online publication date: 4-Mar-2019
https://doi.org/10.1186/s13640-019-0427-6
Grad-Gyenge LFilzmoser P(2017)The Paradigm of RelatednessBusiness Information Systems Workshops10.1007/978-3-319-52464-1_6(57-68)Online publication date: 24-Jan-2017
https://doi.org/10.1007/978-3-319-52464-1_6
Show More Cited By

Index Terms

AnchorMF: towards effective event context identification
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

TWILITE: A recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation

Twitter provides search services to help people find users to follow by recommending popular users or the friends of their friends. However, these services neither offer the most relevant users to follow nor provide a way to find the most interesting ...
Celebrity's self-disclosure on Twitter and parasocial relationships

This study investigated how celebrities' self-disclosure on personal social media accounts, particularly Twitter, affects fans' perceptions. An online survey was utilized among a sample of 429 celebrity followers on Twitter. Results demonstrated that ...
A hybrid collaborative filtering model for social influence prediction in event-based social networks

Event-based social networks (EBSNs) provide convenient online platforms for users to organize, attend and share social events. Understanding users' social influences in social networks can benefit many applications, such as social recommendation and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM'13

Sponsor:

CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

Acceptance Rates

CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
357
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)1

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Magdy A(2020)MicroblogsSIGSPATIAL Special10.1145/3404820.340482712:1(41-52)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3404820.3404827
Wu CLi LPeng CWu YXiong NLee C(2019)Design and analysis of an effective graphics collaborative editing systemEURASIP Journal on Image and Video Processing10.1186/s13640-019-0427-62019:1Online publication date: 4-Mar-2019
https://doi.org/10.1186/s13640-019-0427-6
Grad-Gyenge LFilzmoser P(2017)The Paradigm of RelatednessBusiness Information Systems Workshops10.1007/978-3-319-52464-1_6(57-68)Online publication date: 24-Jan-2017
https://doi.org/10.1007/978-3-319-52464-1_6
Wu EZhang PLu TGu HGu N(2016)Behavior prediction using an improved Hidden Markov Model to support people with disabilities in smart homes2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD.2016.7566051(560-565)Online publication date: May-2016
https://doi.org/10.1109/CSCWD.2016.7566051
Gao LYu FGao LXiong NYang G(2016)Consistency maintenance of compound operations in real-time collaborative environmentsComputers and Electrical Engineering10.1016/j.compeleceng.2015.06.02150:C(217-235)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.compeleceng.2015.06.021
Gao LYu FChen QXiong N(2016)Consistency maintenance of Do and Undo/Redo operations in real-time collaborative bitmap editing systemsCluster Computing10.1007/s10586-015-0499-819:1(255-267)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1007/s10586-015-0499-8
Long YLi VNiu GLu Z(2015)The Impacts of Network Structure on User Activity Level in Online Social NetworksProceedings of the 2015 IEEE / WIC / ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) - Volume 0110.1109/WI-IAT.2015.201(115-122)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.1109/WI-IAT.2015.201
Tang BLu TGu HDing XGu N(2015)Measuring domain-specific user influence in microblogs: An Actor-Network Theory based approach2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD.2015.7230978(314-319)Online publication date: May-2015
https://doi.org/10.1109/CSCWD.2015.7230978
Wang LLu TGu HDing XGu N(2015)Influential user recommendation through SVD based topic diversification2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD.2015.7230954(176-181)Online publication date: May-2015
https://doi.org/10.1109/CSCWD.2015.7230954
Huo HChen SSong LBan LWu ZLiu LGao L(2015)Anomalous Region Detection on the Mobility Data2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing10.1109/CIT/IUCC/DASC/PICOM.2015.252(1669-1674)Online publication date: Oct-2015
https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.252
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents